System for determining quantitative measure of dyadic ties

ABSTRACT

Described are platforms, systems, and methods for determining quantifiable measures of dyadic ties. In one aspect, a method comprises receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.

BACKGROUND

The subject matter of machine learning includes the study of computermodeling of learning processes in their multiple manifestations. Ingeneral, learning processes include various aspects such as theacquisition of new declarative knowledge, the devilment of motor andcognitive skills through instruction or practice, the organization ofnew knowledge into general, effective representations, and the discoveryof new facts and theories through observation and experimentations.Implanting such capabilities in computers has been a goal of computerscientist since the inception of the computer era. However, solving thisproblem has been, and remains, a most challenging goal in artificialintelligence (AI). Unlike human based decision, decision assistancesystems embedded with machine learning algorithms are corruption free asthus are reliable. Achieving an understanding of historical data, theidentification of trends, seasonal patterns, anomalies, emergingpatterns, is time-consuming and prone to errors. Machine learningalgorithms efficiently learn rules thus enabling the identification ofthese signals, and provide accurate predictions on future outcomes.

SUMMARY

Embodiments of the present disclosure are generally directed to a systemthat determines quantifiable measures of dyadic ties between individualsby processing contextual data through a trained machine-learning model.

In today's increasingly disconnected social media world the describeddyadic ties measurement system can be employed to ingest the informationavailable to users of various social media platforms to provide themwith a quantifiable score and other characteristics of individuals intheir life. Analysis of social networks can be employed as a tool forlinking micro and macro levels of sociological theory. This procedure isillustrated by elaboration of the macro implications of one aspect ofsmall-scale interaction: the strength of dyadic ties. In someembodiments, the degree of overlap of two individuals' relationshipnetworks varies directly with the strength of their tie to one another.In some embodiments, the impact of this principle on diffusion ofinfluence and information, mobility opportunity, and communityorganization quantified by the described system. Stress is laid on thecohesive power of weak ties. Most systems and models deal, implicitly,with strong ties, thus confining their applicability to small,well-defined groups. Emphasis on both strong and weak ties (e.g., thestrength of each tie) allows for the described system measure robustlyand accurately the relations between individuals.

Social networking involves internet sites and application whererelationships exist. Examples of such sites include: Facebook™,Linkedln™, Instagram™, Twitter™, Reddit™, Youtube™ Meetup™, Pinterest™,Weibo™, Qzone™, and so forth. However, no platform or system is designedto aggregate data from these disparate sites and provide a measure ofthe relationship between people (e.g., the dyadic ties betweenindividuals) for the individual users of these sites and applications.Moreover, there is no platform or system looking across the digitalfootprint of an individual, ingesting the various data points in orderto build a quantifiable measure of who they know and trust. With thedata lying in multiple companies who are often competitors aggregatingthe data is a difficult task. The described system, however, ingestedand measures these relationships across the digital and real-worldlandscape and provides users feedback based on these measures to assistin determining who knows who and how well.

Accordingly, in a general embodiment, disclosed herein are dyadic tiesmeasurement systems comprising: a user-interface; one or moreprocessors; and a computer-readable storage device coupled to the one ormore processors and having instructions stored thereon which, whenexecuted by the one or more processors, cause the one or more processorsto perform operations. These operations include: receiving contextualdata for a user from at least one data source; processing the contextualdata through a first machine-learning model to determine quantifiablemeasures of dyadic ties between the user and each of a plurality ofindividuals, the first machine-learning model trained with previouslyreceived contextual data of a plurality of other users; determining agrouping for the user based on the determined quantifiable measures, thegrouping comprising at least one of the individuals; and providing,through the user-interface, access to the determined quantifiablemeasures to members of the grouping.

In another general embodiment, one or more non-transitorycomputer-readable storage media are coupled to one or more processors.The one or more non-transitory computer-readable storage media havinginstructions stored thereon which, when executed by the one or moreprocessors, cause the one or more processors to perform operations.These operations include: receiving contextual data for a user from atleast one data source; processing the contextual data through a firstmachine-learning model to determine quantifiable measures of dyadic tiesbetween the user and each of a plurality of individuals, the firstmachine-learning model trained with previously received contextual dataof a plurality of other users; determining a grouping for the user basedon the determined quantifiable measures, the grouping comprising atleast one of the individuals; and providing, through a user-interface,access to the determined quantifiable measures to members of thegrouping.

In yet another general embodiment, methods for determining quantifiablemeasures of dyadic ties are executed by one or more processors. Themethods include: receiving contextual data for a user from at least onedata source; processing the contextual data through a firstmachine-learning model to determine quantifiable measures of dyadic tiesbetween the user and each of a plurality of individuals, the firstmachine-learning model trained with previously received contextual dataof a plurality of other users; determining a grouping for the user basedon the determined quantifiable measures, the grouping comprising atleast one of the individuals; and providing, through a user-interface,access to the determined quantifiable measures to members of thegrouping.

An aspect combinable with the general embodiments, the firstmachine-learning model is retrained with the determined quantifiablemeasures.

In an aspect combinable with any of the previous aspects, thequantifiable measures of dyadic ties are determined based on usercontact detail quality, a frequency of communication, information withincommunications, information capacity and bandwidth, physical distance,social network ties, or timeliness of when contact information wasupdated.

In an aspect combinable with any of the previous aspects, thequantifiable measures of dyadic ties are determined based on usercontact detail quality, a frequency of communication, information withincommunications, information capacity and bandwidth, physical distance,social network ties, or timeliness of when contact information wasupdated.

In an aspect combinable with any of the previous aspects, the firstmachine-learning model determines the quantifiable measures based on acompounding impact of individual elements from the contextual data.

In an aspect combinable with any of the previous aspects, the firstmachine-learning model classifies relationships between the user andeach of the individuals according to type, length, and age of therespective parties at a time when the respective relationship began.

In an aspect combinable with any of the previous aspects, the firstmachine-learning model comprises weighted values for theclassifications.

In an aspect combinable with any of the previous aspects, the operationsinclude: before processing the contextual data through the firstmachine-learning model: receiving validation data for the user from atleast one data enricher; and processing the contextual data and thevalidation data through a second machine-learning model to determinecontact information for the user and the individuals, the secondmachine-learning model trained with previously received validation dataand the previously received contextual data of the other users.

In an aspect combinable with any of the previous aspects, the receivedvalidation data and the determined contact information is processedthrough the first machine-learning model to determine the quantifiablemeasures.

In an aspect combinable with any of the previous aspects, the secondmachine-learning model merges the processed data to determine and verifycurrent and previous contact information for the user and theindividuals.

In an aspect combinable with any of the previous aspects, the secondmachine-learning model merges the processed data to determine achorological order of the contact information.

In an aspect combinable with any of the previous aspects, the operationsinclude: receiving, from the user-interface, corrections for thedetermined contact information.

In an aspect combinable with any of the previous aspects, the firstmachine-learning model is retrained with the corrections.

In an aspect combinable with any of the previous aspects, the operationsinclude: receiving, from the user-interface, instructions to remove theaccess to at least one of the determined measures.

In an aspect combinable with any of the previous aspects, the contextualdata is received from at least one source data provider.

In an aspect combinable with any of the previous aspects, the contextualdata is received via an application programming interface (API).

In an aspect combinable with any of the previous aspects, the at leaston source data provider comprises a social media provides, an emailprovider, the user's phone contacts, a messaging provider, a provider ofat least one forum, a provider of an auction or selling site, or aprovider of a recreational site.

In an aspect combinable with any of the previous aspects, the contextualdata is received on a periodic basis.

In an aspect combinable with any of the previous aspects, the contextualdata is received on daily or weekly.

In an aspect combinable with any of the previous aspects, the groupingincludes the user.

In an aspect combinable with any of the previous aspects, the operationsinclude: providing, to the user-interface, an industry strength scoringfor the user determined according to the determined quantifiablemeasures.

In an aspect combinable with any of the previous aspects, the operationsinclude: providing, to the user-interface, a regional strength scoringfor the user determined according to the determined quantifiablemeasures.

In an aspect combinable with any of the previous aspects, the operationsinclude: providing, to the user-interface, a best path to a decisionmaker determined according to the determined quantifiable measures.

In an aspect combinable with any of the previous aspects, the operationsinclude: providing, to the user-interface, a validation of a recruitingrolodex determined according to the determined quantifiable measures.

In an aspect combinable with any of the previous aspects, the groupingincludes a plurality of teams.

In an aspect combinable with any of the previous aspects, the access tothe determined quantifiable measures is provided based on permissionsreceived from the user-interface.

In an aspect combinable with any of the previous aspects, the operationsinclude: providing the access to the determined quantifiable measures toa calendar application or an email client accessible by at least one ofthe members of the grouping or the user.

In an aspect combinable with any of the previous aspects, the contextualdata comprises digital footprint data for the user and digital path datafor the user.

Particular embodiments of the subject matter described in thisdisclosure can be implemented so as to realize one or more of thefollowing advantages. The described system receives contextual data fromdisparate sources and employs machine learning determine a quantifiablemeasure of the dyadic ties between individuals. In some embodiments, amachine-learning model is trained to quantify the compounding impact ofindividual elements from the received contextual data as it related tothe dyadic ties between individuals.

It is appreciated that methods in accordance with the present disclosurecan include any combination of the aspects and features describedherein. That is, methods in accordance with the present disclosure arenot limited to the combinations of aspects and features specificallydescribed herein, but also may include any combination of the aspectsand features provided.

The details of one or more embodiments of the present disclosure are setforth in the accompanying drawings and the description below. Otherfeatures and advantages of the present disclosure will be apparent fromthe description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the features and advantages of the presentsubject matter will be obtained by reference to the following detaileddescription that sets forth illustrative embodiments and theaccompanying drawings of which:

FIGS. 1A-1B depict various non-limiting exemplary weighted graphs;

FIG. 2 depicts a non-limiting exemplary architecture of the describeddyadic ties measurement system;

FIGS. 3A-3C depict various non-limiting exemplary pages of a userinterface (UI) by the described dyadic ties measurement system;

FIG. 4 depict flowcharts of a non-limiting exemplary process that can beimplemented by embodiments of the present disclosure;

FIG. 5 depicts a non-limiting exemplary computer system that can beprogrammed or otherwise configured to implement methods or systems ofthe present disclosure;

FIG. 6A depicts a non-limiting example environment that can be employedto execute embodiments of the present disclosure;

FIG. 6B depicts a non-limiting example application provision system thatcan be provided through an environment and employed to executeembodiments of the present disclosure; and

FIG. 6C depicts a non-limiting example cloud-based architecture of anapplication provision system that can be provided through an environmentand employed to execute embodiments of the present disclosure.

DETAILED DESCRIPTION

Described herein, in certain embodiments, are dyadic ties measurementsystems comprising: a user-interface; one or more processors; and acomputer-readable storage device coupled to the one or more processorsand having instructions stored thereon which, when executed by the oneor more processors, cause the one or more processors to performoperations comprising: receiving contextual data for a user from atleast one data source; processing the contextual data through a firstmachine-learning model to determine quantifiable measures of dyadic tiesbetween the user and each of a plurality of individuals, the firstmachine-learning model trained with previously received contextual dataof a plurality of other users; determining a grouping for the user basedon the determined quantifiable measures, the grouping comprising atleast one of the individuals; and providing, through a user-interface,access to the determined quantifiable measures to members of thegrouping.

Also described herein, in certain embodiments, are non-transitorycomputer-readable storage media coupled to one or more processors andhaving instructions stored thereon which, when executed by the one ormore processors, cause the one or more processors to perform aoperations comprising: receiving contextual data for a user from atleast one data source; processing the contextual data through a firstmachine-learning model to determine quantifiable measures of dyadic tiesbetween the user and each of a plurality of individuals, the firstmachine-learning model trained with previously received contextual dataof a plurality of other users; determining a grouping for the user basedon the determined quantifiable measures, the grouping comprising atleast one of the individuals; and providing, through a user-interface,access to the determined quantifiable measures to members of thegrouping.

Also described herein, in certain embodiments, are computer-implementedmethods for determining quantifiable measures of dyadic ties comprising:receiving contextual data for a user from at least one data source;processing the contextual data through a first machine-learning model todetermine quantifiable measures of dyadic ties between the user and eachof a plurality of individuals, the first machine-learning model trainedwith previously received contextual data of a plurality of other users;determining a grouping for the user based on the determined quantifiablemeasures, the grouping comprising at least one of the individuals; andproviding, through a user-interface, access to the determinedquantifiable measures to members of the grouping.

Certain Definitions

Unless otherwise defined, all technical terms used herein have the samemeaning as commonly understood by one of ordinary skill in the art towhich the present subject matter belongs. As used in this specificationand the appended claims, the singular forms “a,” “an,” and “the” includeplural references unless the context clearly dictates otherwise. Anyreference to “or” herein is intended to encompass “and/or” unlessotherwise stated.

As used herein, the term “real-time” refers to transmitting orprocessing data without intentional delay given the processinglimitations of a system, the time required to accurately obtain data andimages, and the rate of change of the data and images. In some examples,“real-time” is used to describe the presentation of information obtainedfrom components of embodiments of the present disclosure.

Contextual data includes data that provides context to a person, entity,or group. Contextual data can be taken from various sources, such as,websites, email, social media, socioeconomic background, educationalhistory, work history and so forth. In some embodiments, contextual dataincludes Digital path data and/or digital footprint data.

Digital path data includes programs, websites, service, and systems thatindividuals (e.g., users) use. For example, a system that is part of auser's daily routine.

Digital footprint data includes the “trail” of data that a user createswhile using programs, websites, service, and systems (e.g., when auser's users the Internet). Digital footprint data includes, forexample, websites visited, emails sent, and information submitted toonline services.

A data enricher includes providers and processes used to enhance,refine, or otherwise improve raw data. A data enricher increases thevalue of data and shows a common imperative of proactively using thisdata in various ways.

Ties include the multidimensional social relationships with co-workers,peers, neighbors, and others that an individual builds. Such ties yieldsocial capital that can be drawn upon for information, resources, andsupport.

A strong tie is someone whom an individual knows well. For example,someone for whom the individual has a number his/her phone or with whomhe/she interacts on social networking sites. For individuals with strongties between them, there is good two-way communication, and even if theydo not know everything about one other, they know each other well enoughsuch that information flows freely. Additionally, individuals withstrong ties typically know the same information.

An industry strength scoring includes a score based on the cumulativetotal of relationship scores within a given industry or vertical.

Regional strength scoring includes a score based on the cumulative totalof relationship scores within a given region or territory.

Dyadic Ties Measurement System

Embodiments of the present disclosure are generally directed to systems,platforms, and methods for determining quantifiable measures of dyadicties between individuals. More particularly, embodiments of the presentdisclosure are directed to a system that enables a user to providemembers (e.g., other system users) access to quantifiable measures ofdyadic ties between themselves and other individuals with whom the userinteracts. In some embodiments, the described dyadic ties measurementsystem includes a machine-learning model trained with previouslyreceived contextual data (e.g., of system users or anonymous data). Insome embodiments, contextual data is received based on permissions andaccess granted by the user. In some embodiments, the contextual data isprocessed by the trained machine-learning model to determine thequantifiable measures of dyadic ties between the user individuals. Insome embodiments, the quantifiable measures are employed to determinethe strength of relationships, provide industry strength scoring andregional strength scoring, determine a path to decision makers, requestintroductions through the platform, determine a team or social/businessgrouping, and so forth. In some embodiments, the content of thecontextual data is enhanced by data enrichers, which provide, forexample, deduplication and chronological information.

FIGS. 1A and 1B depict example weighted graphs 100 and 110 respectively.The example weighted graph 100 depicts two circles 102 representingactors A and B. The line 104 connecting the circles 102 shows anassigned value. In some embodiments, the assigned value represents a tiestrength or a measure of the dyadic ties between actors A and B betweenthe actors A and B. Tie strength can attach values to ties, representingquantitative attributes, strength of relationship, frequency ofcommunication, information of communication, information capacity andbandwidth, or physical distance.

The example weighted graph 110 is a more complicated graph and depictscircles 102 that representing a set of actors A, B, C, D, E, and F andlines 114 that represent a set of dyadic ties between each of theactors. In some embodiments, the described system processes the massiveamount of information that lives on the Internet and bullet down to itstwo smallest forms, the dyadic relationship between individuals. Ameasure (e.g., a score value from 0 to 100 for each relationship betweenindividuals is determined through a trained machine learning model andprovided to users of the system. In some embodiments, the measuresprovide a user with information regarding the people he or she knows andhow well he or she you knows them.

In some embodiments, one important aspect represented through theinformation in the depicted weighted graphs 100 and 110 is a retakingthe available data available from a plethora a systems and service todetermine who a user's actual real-life friends are and not with whom,for example, a social media platform thinks the user should connect,friend, or follow.

FIG. 2 depicts an example architecture 200 for the described dyadic tiesmeasurement system. As depicted, the example architecture 200 includesdata sources 202, data enrichers 204, user device 206, and a dyadic tiesmeasurement system 210. As depicted, the dyadic ties measurement system210 includes a collection engine 212, an ingestion engine 214, servicesmodules 216, a contextual data store 218, and a dyadic ties data store219. The contextual data store 218 and the dyadic ties data store 219may be any suitable type of data store, such as a database. In someembodiments, the contextual data store 218 and the dyadic ties datastore 219 may be comprised within the same data store. Data stores aredescribed in greater detail below in the data store section.

In some embodiments, the dyadic ties measurement system 210 receives orretrieves contextual data for users from the data sources 202. In someembodiments, the dyadic ties measurement system 210 retrieves thecontextual data via an API. In some embodiments, users grant permissionto the dyadic ties measurement system 210 to receive or retrieve thecontextual data from the data sources 202. In some embodiments, usersprovide credential to the dyadic ties measurement system 210 for atleast one of the data sources 202. Example data sources 202 include, butare not limited to, exchange servers, cloud-based Office 365, analyticswebsites, social media websites. In some embodiments, the receivedcontextual data includes personal, work, and recreational data forusers.

In some embodiments, the dyadic ties measurement system 210 employs thedata enrichers 204 to provide additional information (e.g., validationdata) regarding users. Such additional data can be employed to, forexample, clean up (e.g., merge) contact information. In someembodiments, the data enrichers 204 provide services though an API. Insome embodiments, the services provided by the data enrichers 204 areinvoked using a key value pair, such as First Name, Last Name, and City.In some embodiments, the data enrichers 204 provide the dyadic tiesmeasurement system 210 enriched and verified information that mayinclude, for example, an individual's current city, full legal name, andso forth. In some embodiments, the data enrichers 204 provideinformation for deduplication of information for an individual. In someembodiments, the data enrichers 204 provide historical information aboutan individual, such as pervious addresses or phone numbers.

User device 206 can include any appropriate type of computing device.Such computing devices are described in greater detail below in thesection describing the computing devices 602, 604, and 606 depicted inFIG. 6A. In some embodiments, the dyadic ties measurement system 210receives or retrieves contextual data for users from user devices, suchas user device 206. For example, the dyadic ties measurement system 210may access the user device 206 to retrieve a list of contacts.

In some embodiments, a respective user associate with the user device206 may provide credentials or permissions to access the user device206. In some embodiments, the dyadic ties measurement system 210, viathe collection engine 212, employs the validation data received from thedata enrichers 204 to clean up the contact data. In some embodiments,the dyadic ties measurement system 210 may receive responses to a seriesof questions (e.g., who is the best supervisor or peer with whom youhave worked) from the user device 206. In some embodiments, the dyadicties measurement system 210 may receive reviews or evaluations, such aspeer reviews, from the user device 206.

In some embodiments, the collection engine 212 collects the contextualdata from the above described data sources 202, data enrichers 204, userdevice 206; processes the received data; and persists the processed datato the contextual data store 218. In some embodiments, the collectionengine 212 collects data by pulling from the sources (e.g., by callingan API). In some embodiments, the data is pushed from the sources (e.g.,via an API provided by the system) to the collection engine 212. In someembodiments, the data is collected periodically, such as hourly, daily,or weekly.

In some embodiments, the collection engine 212 processes the receivedcontextual data and validation data through a machine-learning model toclean up (e.g., merge, order, validate, verify, filter, etc.) the datacontextual. For example, the collection engine 212 may determine currentand historical contact information for a user by processing the receiveddata through the machine-learning model. In some embodiments, themachine-learning model employed by the collection engine 212 is trainedwith previously received validation data and contextual data. In someembodiments, users can manually clean up the received data via the userdevice 206 through a UI, such as the UI depicted in FIGS. 3A-3C.

In some embodiments, the ingestion engine 214 processes the collectedcontextual data persisted to the contextual data store 218 to determinedquantifiable measures of dyadic ties between individuals. In someembodiments, the ingestion engine 214 processes the contextual datathrough a machine-learning model to determine quantifiable measures ofdyadic ties between users and individuals. For example, themachine-learning model can be process a user's daily digital path fromwork communications or personal daily digital journeys from personalemail communication. As another example, the model can process arecreational digital path of a user, such as what the user follows ordoes on social media, the team that he or she follows on a sports site,fantasy or recreational leagues and/or clubs of which the user is amember, and so forth. In some embodiments, based on this information,the model can determine a consolidated snapshot of who a person knowsand how well he or she knows them. This information can be employed todetermine the quantifiable measures of dyadic ties between theindividuals.

The machine-learning model may also ingest the digital footprints ofusers on a daily basis to determine who a user knows and how well. Insome embodiments, the measures of dyadic ties between individuals aredetermined based on the accuracy or timeliness of the information aparticular individual has for another. For example, how accurate andup-to-date a contact record is for a person (e.g., does the contactinclude the person's middle name, does the contact include a person'snew address) is an indication of how well the contact holder knows thatperson. In some embodiments, the determined measures of dyadic tiesbetween individuals are determined based on the type and/or length of arelationship. For example, determining that a person is a family memberor friend from childhood friend indicates a strong dyadic tie. Othertypes of information collected and processed by the system include: whoa person messages and how often; whether contact data is both valid andcurrent; does the contact data include a personal phone number or email;does the contact data include any historical information, such as an oldaddress; and so forth.

In some implementations, the machine-learning model quantifies acompounding impact of the received contextual data to determine themeasures of dyadic ties. Once the contextual data is processes, thedetermined measures indicate who a person knows and how much trustexists between them. In some implementations, the trainedmachine-learning model includes weighted values. For example, detailssuch as the amount of historical information, the length of arelationship, how current is the connection, the type of informationexchanged and discussed, can each be weighted respectively to eachother. In some embodiments, the ingestion engine 214 stores thedetermined measures along with other relevant user data to the dyadicties data store 219.

The services modules 216 provide services to system users. In someembodiments, services are provided through a UI, such as depicted inFIG. 3A-3C. For example, users can correct a value or remove aconnection using the UI. In some embodiments, services modules 216provide the measures to calendar application or email clients (e.g., auser can see who is connected to clients or people with whom he or shehas a meeting).

In some embodiments, a user is grouped with other users into teams. Insome embodiments, the members of these teams may be based on settingsprovided to the dyadic ties measurement system 210 through, for example,an administrative UI. In some embodiments, users may select which teamsto join. In some embodiments, teams may be determined automatically bythe system 210 based on the determined measures. For example, a salesteam may set up so that the members can pool contact information(connections), accessed through the services modules 216, to see who thevarious team members know and how well.

Example Processes

FIG. 4 depicts a flowchart of an example process 400 that can beimplemented by embodiments of the present disclosure. The exampleprocess 400 can be implemented by the components of the described dyadicties measurement system, such as described above in FIG. 2 . The exampleprocess 400 generally shows in more detail how quantifiable measures ofdyadic ties between users can be determined by processing contextualdata through a first machine-learning model and employed within thedescribed system.

For clarity of presentation, the description that follows generallydescribes the example process 400 in the context of FIGS. 1-3C, and5-6C. However, it will be understood that the process 400 may beperformed, for example, by any other suitable system, environment,software, and hardware, or a combination of systems, environments,software, and hardware as appropriate. In some embodiments, variousoperations of the process 400 can be run in parallel, in combination, inloops, or in any order.

At 402, contextual data for a user is received from at least one datasource. In some embodiments, the contextual data is received from atleast one source data provider. In some embodiments, the contextual datais received via an API. In some embodiments, the at least on source dataprovider comprises a social media provides, an email provider, theuser's phone contacts, a messaging provider, a provider of at least oneforum, a provider of an auction or selling site, or a provider of arecreational site. In some embodiments, the contextual data is receivedon a periodic basis. In some embodiments, the contextual data isreceived on daily or weekly. In some embodiments, the contextual datacomprises digital footprint data for the user and digital path data forthe user. From 402, the process 400 proceeds to 404.

At 404, the contextual data is processed through a firstmachine-learning model to determine quantifiable measures of dyadic tiesbetween the user and each of a plurality of individuals. In someembodiments, the first machine-learning model is trained with previouslyreceived contextual data of a plurality of other users. In someembodiments, the first machine-learning model is retrained with thedetermined quantifiable measures. In some embodiments, the quantifiablemeasures of dyadic ties are determined based on user contact detailquality, a frequency of communication, information withincommunications, information capacity and bandwidth, physical distance,social network ties, or timeliness of when contact information wasupdated. In some embodiments, the first machine-learning modeldetermines the quantifiable measures based on a compounding impact ofindividual elements from the contextual data. In some embodiments, thefirst machine-learning model classifies relationships between the userand each of the individuals according to type, length, and age of therespective parties at a time when the respective relationship began. Insome embodiments, the first machine-learning model comprises weightedvalues for the classifications. In some embodiments, before processingthe contextual data through the first machine-learning model, validationdata for the user is received from at least one data enricher, and thecontextual data and the validation data is processed through a secondmachine-learning model to determine contact information for the user andthe individuals, the second machine-learning model trained withpreviously received validation data and the previously receivedcontextual data of the other users. In some embodiments, the receivedvalidation data and the determined contact information is processedthrough the first machine-learning model to determine the quantifiablemeasures. In some embodiments, the second machine-learning model mergesthe processed data to determine and verify current and previous contactinformation for the user and the individuals. In some embodiments, thesecond machine-learning model merges the processed data to determine achorological order of the contact information. In some embodiments,corrections for the determined contact information are received from theuser-interface. In some embodiments, the first machine-learning model isretrained with the corrections. From 404, the process 400 proceeds to406.

At 406, a grouping for the user is determined based on the determinedquantifiable measures, the grouping comprising at least one of theindividuals. In some embodiments, the grouping includes the user. Insome embodiments, the grouping comprises a team. From 406, the process400 proceeds to 408.

At 408, access to the determined quantifiable measures is provided tomembers of the grouping through a UI. In some embodiments, instructionsto remove the access to at least one of the determined measures arereceived from the UI. In some embodiments, an industry strength scoringfor the user determined according to the determined quantifiablemeasures is provided to the UI. In some embodiments, a regional strengthscoring for the user determined according to the determined quantifiablemeasures is provided to the UI. In some embodiments, a best path to adecision maker determined according to the determined quantifiablemeasures is provided to the UI. In some embodiments, a validation of arecruiting rolodex determined according to the determined quantifiablemeasures is provided to the UI. In some embodiments, the access to thedetermined quantifiable measures is provided based on permissionsreceived from the UI. In some embodiments, the access to the determinedquantifiable measures is provided to a calendar application or an emailclient accessible by at least one of the members of the grouping or theuser. From 408, the process 400 ends.

Processing Devices and Processors

In some embodiments, the platforms, systems, media, and methodsdescribed herein include a computer, or use of the same. In furtherembodiments, the computer includes one or more hardware centralprocessing units (CPUs) or general purpose graphics processing units(GPGPUs) that carry out the device's functions. In still furtherembodiments, the computer comprises an operating system configured toperform executable instructions. In some embodiments, the computer isoptionally connected a computer network. In further embodiments, thecomputer is optionally connected to the Internet such that it accessesthe World Wide Web. In still further embodiments, the computer isoptionally connected to a cloud computing infrastructure. In otherembodiments, the computer is optionally connected to an intranet. Inother embodiments, the computer is optionally connected to a datastorage device.

In accordance with the description herein, suitable computers include,by way of non-limiting examples, server computers, desktop computers,laptop computers, notebook computers, sub-notebook computers, netbookcomputers, netpad computers, handheld computers, Internet appliances,mobile smartphones, tablet computers, and vehicles. Those of skill inthe art will recognize that many smartphones are suitable for use in thesystem described herein. Those of skill in the art will also recognizethat select televisions, video players, and digital music players withoptional computer network connectivity are suitable for use in thesystem described herein. Suitable tablet computers include those withbooklet, slate, and convertible configurations, known to those of skillin the art.

In some embodiments, the computer includes an operating systemconfigured to perform executable instructions. The operating system is,for example, software, including programs and data, which manages thedevice's hardware and provides services for execution of applications.Those of skill in the art will recognize that suitable server operatingsystems include, by way of non-limiting examples, FreeBSD, OpenBSD,NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, WindowsServer®, and Novell® NetWare®. Those of skill in the art will recognizethat suitable personal computer operating systems include, by way ofnon-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, andUNIX-like operating systems such as GNU/Linux. In some embodiments, theoperating system is provided by cloud computing. Those of skill in theart will also recognize that suitable mobile smart phone operatingsystems include, by way of non-limiting examples, Nokia® Symbian® OS,Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®,Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, andPalm® WebOS®.

In some embodiments, the device includes a storage or memory device. Thestorage or memory device is one or more physical apparatuses used tostore data or programs on a temporary or permanent basis. In someembodiments, the device is volatile memory and requires power tomaintain stored information. In some embodiments, the device isnon-volatile memory and retains stored information when the computer isnot powered. In further embodiments, the non-volatile memory comprisesflash memory. In some embodiments, the volatile memory comprises dynamicrandom-access memory (DRAM). In some embodiments, the non-volatilememory comprises ferroelectric random access memory (FRAM). In someembodiments, the non-volatile memory comprises phase-change randomaccess memory (PRAM). In other embodiments, the device is a storagedevice including, by way of non-limiting examples, compact discread-only memory (CD-ROM), digital versatile disc (DVD), flash memorydevices, magnetic disk drives, magnetic tapes drives, optical diskdrives, and cloud computing based storage. In further embodiments, thestorage and/or memory device is a combination of devices such as thosedisclosed herein.

In some embodiments, the computer includes a display to send visualinformation to a user. In some embodiments, the display is a liquidcrystal display (LCD). In further embodiments, the display is a thinfilm transistor liquid crystal display (TFT-LCD). In some embodiments,the display is an organic light emitting diode (OLED) display. Invarious further embodiments, on OLED display is a passive-matrix OLED(PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments,the display is a plasma display. In other embodiments, the display is avideo projector. In yet other embodiments, the display is a head-mounteddisplay in communication with a computer, such as a virtual reality (VR)headset. In further embodiments, suitable VR headsets include, by way ofnon-limiting examples, HTC Vive, Oculus Rift, Samsung Gear VR, MicrosoftHoloLens, Razer Open-Source Virtual Reality (OSVR), FOVE VR, Zeiss VROne, Avegant Glyph, Freefly VR headset, and the like. In still furtherembodiments, the display is a combination of devices such as thosedisclosed herein.

In some embodiments, the computer includes an input device to receiveinformation from a user. In some embodiments, the input device is akeyboard. In some embodiments, the input device is a pointing deviceincluding, by way of non-limiting examples, a mouse, trackball, trackpad, joystick, game controller, or stylus. In some embodiments, theinput device is a touch screen or a multi-touch screen. In otherembodiments, the input device is a microphone to capture voice or othersound input. In other embodiments, the input device is a video camera orother sensor to capture motion or visual input. In further embodiments,the input device is a Kinect, Leap Motion, or the like. In still furtherembodiments, the input device is a combination of devices such as thosedisclosed herein.

Computer control systems are provided herein that can be used toimplement the platforms, systems, media, and methods of the disclosure.FIG. 5 depicts an example computer system 500 that can be programmed orotherwise configured to implement platforms, systems, media, and methodsof the present disclosure. For example, the computing device 510 can beprogrammed or otherwise configured to display a user-interface orapplication provided by the described dyadic ties measurement system.

In the depicted embodiment, the computing device 510 includes a CPU(also “processor” and “computer processor” herein) 512, which isoptionally a single core, a multi core processor, or a plurality ofprocessors for parallel processing. The computing device 510 alsoincludes memory or memory location 517 (e.g., random-access memory,read-only memory, flash memory), electronic storage unit 514 (e.g., harddisk), communication interface 515 (e.g., a network adapter) forcommunicating with one or more other systems, and peripheral devices516, such as cache, other memory, data storage and/or electronic displayadapters. In some embodiments, the memory 517, storage unit 514,communication interface 515, and peripheral devices 516 are incommunication with the CPU 512 through a communication bus (solidlines), such as a motherboard. The storage unit 514 comprises a datastorage unit (or data repository) for storing data. The computing device510 is optionally operatively coupled to a computer network, such as thenetwork 610 depicted and described in FIG. 6A, with the aid of thecommunication interface 515. In some embodiments, the computing device510 is configured as a back-end server deployed within the describeddyadic ties measurement system.

In some embodiments, the CPU 512 can execute a sequence ofmachine-readable instructions, which can be embodied in a program orsoftware. The instructions may be stored in a memory location, such asthe memory 517. The instructions can be directed to the CPU 512, whichcan subsequently program or otherwise configure the CPU 512 to implementmethods of the present disclosure. Examples of operations performed bythe CPU 512 can include fetch, decode, execute, and write back. In someembodiments, the CPU 512 is part of a circuit, such as an integratedcircuit. One or more other components of the computing device 510 can beoptionally included in the circuit. In some embodiments, the circuit isan application specific integrated circuit (ASIC) or a FPGA.

In some embodiments, the storage unit 514 stores files, such as drivers,libraries and saved programs. In some embodiments, the storage unit 514stores user data, e.g., user preferences and user programs. In someembodiments, the computing device 510 includes one or more additionaldata storage units that are external, such as located on a remote serverthat is in communication through an intranet or the Internet.

In some embodiments, the computing device 510 communicates with one ormore remote computer systems through a network. For instance, thecomputing device 510 can communicate with a remote computer system.Examples of remote computer systems include personal computers (e.g.,portable PC), slate or tablet PCs (e.g., Apple® iPad, Samsung® GalaxyTab, etc.), smartphones (e.g., Apple® iPhone, Android-enabled device,Blackberry®, etc.), or personal digital assistants. In some embodiments,a user can access the computing device 510 via a network.

In some embodiments, the platforms, systems, media, and methods asdescribed herein are implemented by way of machine (e.g., computerprocessor) executable code stored on an electronic storage location ofthe computing device 510, such as, for example, on the memory 517 or theelectronic storage unit 514. In some embodiments, the CPU 512 is adaptedto execute the code. In some embodiments, the machine executable ormachine readable code is provided in the form of software. In someembodiments, during use, the code is executed by the CPU 512. In someembodiments, the code is retrieved from the storage unit 514 and storedon the memory 517 for ready access by the CPU 512. In some situations,the electronic storage unit 514 is precluded, and machine-executableinstructions are stored on the memory 517. In some embodiments, the codeis pre-compiled. In some embodiments, the code is compiled duringruntime. The code can be supplied in a programming language that can beselected to enable the code to execute in a pre-compiled or as-compiledfashion.

In some embodiments, the computing device 510 can include or be incommunication with an electronic display 520. In some embodiments, theelectronic display 520 provides a UI 525 that depicts various screensuch as the examples depicted in FIGS. 3A-3D.

FIG. 6A depicts an example environment 600 that can be employed toexecute embodiments of the present disclosure. The example system 600includes computing devices 602, 604, 606, a back-end system 630, and anetwork 610. In some embodiments, the network 610 includes a local areanetwork (LAN), wide area network (WAN), the Internet, or a combinationthereof, and connects web sites, devices (e.g., the computing devices602, 604, and 606) and back-end systems (e.g., the back-end system 630).In some embodiments, the network 610 includes the Internet, an intranet,an extranet, or an intranet and/or extranet that is in communicationwith the Internet. In some embodiments, the network 610 includes atelecommunication or a data network. In some embodiments, the network610 can be accessed over a wired or a wireless communications link. Forexample, mobile computing devices (e.g., the smartphone device 602 andthe tablet device 606), can use a cellular network to access the network610.

The described dyadic ties measurement system may be employed within theexample environment 600 to, for example, employ machine learning/AItechniques for processing contextual data through a machine-learningalgorithm to quantifiable measures of dyadic ties between users, themachine learning algorithm having been trained with received contextualdata.

In some examples, the users 622, 624, and 626 interact with thedescribed dyadic ties measurement system through a graphical userinterface (GUI), such as depicted in FIGS. 3A-3C, or application that isinstalled and executing on their respective computing devices 602, 604,and 606. In some examples, the computing devices 602, 604, and 606provide viewing data to screens with which the users 622, 624, and 626,can interact. In some embodiments, the computing devices 602, 604, 606are sustainably similar to computing device 510 depicted in FIG. 5 . Thecomputing devices 602, 604, 606 may each include any appropriate type ofcomputing device, such as a desktop computer, a laptop computer, ahandheld computer, a tablet computer, a personal digital assistant(PDA), a cellular telephone, a network appliance, a camera, a smartphone, an enhanced general packet radio service (EGPRS) mobile phone, amedia player, a navigation device, an email device, a game console, oran appropriate combination of any two or more of these devices or otherdata processing devices. Three user computing devices 602, 604, and 606are depicted in FIG. 6A for simplicity. In the depicted exampleenvironment 600, the computing device 602 is depicted as a smartphone,the computing device 604 is depicted as a tablet-computing device, andthe computing device 606 is depicted a desktop computing device. It iscontemplated, however, that embodiments of the present disclosure can berealized with any of the appropriate computing devices, such as thosementioned previously. Moreover, embodiments of the present disclosurecan employ any number of devices as required.

In the depicted example environment 600, the back-end system 630includes at least one server device 632 and at least one data store 634.In some embodiments, the device 632 is sustainably similar to computingdevice 510 depicted in FIG. 5 . In some embodiments, the back-end system630 may include server-class hardware type devices. In some embodiments,the server device 632 is a server-class hardware type device. In someembodiments, the back-end system 630 includes computer systems usingclustered computers and components to act as a single pool of seamlessresources when accessed through the network 610. For example, suchembodiments may be used in data center, cloud computing, storage areanetwork (SAN), and network attached storage (NAS) applications. In someembodiments, the back-end system 630 is deployed using a virtualmachine(s). In some embodiments, the data store 634 is a repository forpersistently storing and managing collections of data. Example datastore that may be employed within the described dyadic ties measurementsystem include data repositories, such as a database as well as simplerstore types, such as files, emails, and so forth. In some embodiments,the data store 634 includes a database. In some embodiments, a databaseis a series of bytes or an organized collection of data that is managedby a database management system (DBMS).

In some embodiments, the at least one server system 632 hosts one ormore computer-implemented services, such as described above, provided bythe described dyadic ties measurement system that users 622, 624, and626 can interact with using the respective computing devices 602, 604,and 606.

FIG. 6B depicts an example application provision system 640 that can beprovided through an environment, such as the example environment 600 andemployed to execute embodiments of the present disclosure. As depicted,the example application provision system 640 includes the back-endsystem 630 configured to include one or more data stores 634 accessed bya DBMS 648. Suitable DBMSs include Firebird, MySQL, PostgreSQL, SQLite,Oracle Database, Microsoft SQL Server, IBM DB2, IBM Informix, SAPSybase, SAP Sybase, Teradata, and the like. As depicted, the exampleapplication provision system 640 includes the back-end system 630configured to include one or more application severs 646 (such as Javaservers, .NET servers, PHP servers, and the like) and one or more webservers 642 (such as Apache, IIS, GWS and the like). The web server(s)642 optionally expose one or more web services via an API 644 via thenetwork 610. In some embodiments, the example application provisionsystem 640 provides browser-based or mobile native UIs to the computingdevices 602, 604, 606.

FIG. 6C depicts an example cloud-based architecture of an applicationprovision system 650 that can be provided through an environment, suchas the example environment 600, and employed to execute embodiments ofthe present disclosure. The application provision system 650 includesthe back-end system 630 configured to include elastically load balanced,auto-scaling web server resources 672, application server resources 674,as well as synchronously replicated stores 676. In some embodiment, ofthe example cloud-based architecture of an application provision system650, content 662 of services are provided through a content deliverynetwork (CDN) 660 coupled with the back-end system 630. In someembodiments, a CDN is a geographically distributed network of proxyservers and respective data centers that provides high availability andhigh performance through distributing the service spatially relative tothe receiving devices, such as commuting devices 602, 604, and 606.

Non-Transitory Computer Readable Storage Medium

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include one or more non-transitory computer readablestorage media encoded with a program including instructions executableby the operating system of an optionally networked computer. In furtherembodiments, a computer readable storage medium is a tangible componentof a computer. In still further embodiments, a computer readable storagemedium is optionally removable from a computer. In some embodiments, acomputer readable storage medium includes, by way of non-limitingexamples, CD-ROMs, DVDs, flash memory devices, solid state memory,magnetic disk drives, magnetic tape drives, optical disk drives, cloudcomputing systems and services, and the like. In some cases, the programand instructions are permanently, substantially permanently,semi-permanently, or non-transitorily encoded on the media.

Computer Program

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include at least one computer program, or use of thesame. A computer program includes a sequence of instructions, executablein the computer's CPU, written to perform a specified task. Computerreadable instructions may be implemented as program modules, such asfunctions, objects, API, data structures, and the like, that performparticular tasks or implement particular abstract data types. In lightof the disclosure provided herein, those of skill in the art willrecognize that a computer program may be written in various versions ofvarious languages.

The functionality of the computer readable instructions may be combinedor distributed as desired in various environments. In some embodiments,a computer program comprises one sequence of instructions. In someembodiments, a computer program comprises a plurality of sequences ofinstructions. In some embodiments, a computer program is provided fromone location. In other embodiments, a computer program is provided froma plurality of locations. In various embodiments, a computer programincludes one or more software modules. In various embodiments, acomputer program includes, in part or in whole, one or more webapplications, one or more mobile applications, one or more standaloneapplications, one or more web browser plug-ins, extensions, add-ins, oradd-ons, or combinations thereof.

Machine Learning

In some embodiments, machine learning algorithms are employed to build amodel to determine quantifiable measures of dyadic ties between theindividuals. In some embodiments, machine learning algorithms areemployed to build a model to determine the filter relevant orchorological contact information for a user. Examples of machinelearning algorithms may include a support vector machine (SVM), a naïveBayes classification, a random forest, a neural network, deep learning,or other supervised learning algorithm or unsupervised learningalgorithm for classification and regression. The machine learningalgorithms may be trained using one or more training datasets. Forexample, previously received contextual data may be employed to trainvarious algorithms. Moreover, as described above, these algorithms canbe continuously trained/retrained using real-time user data as it isreceived. In some embodiments, the machine learning algorithm employsregression modelling where relationships between variables aredetermined and weighted. In some embodiments, the machine learningalgorithm employ regression modelling, wherein relationships betweenpredictor variables and dependent variables are determined and weighted.

Web Application

In some embodiments, a computer program includes a web application. Inlight of the disclosure provided herein, those of skill in the art willrecognize that a web application, in various embodiments, utilizes oneor more software frameworks and one or more database systems. In someembodiments, a web application is created upon a software framework suchas Microsoft®.NET or Ruby on Rails (RoR). In some embodiments, a webapplication utilizes one or more database systems including, by way ofnon-limiting examples, relational, non-relational, object oriented,associative, and eXtensible Markup Language (XML) database systems. Infurther embodiments, suitable relational database systems include, byway of non-limiting examples, Microsoft® SQL Server, mySQL™, andOracle®. Those of skill in the art will also recognize that a webapplication, in various embodiments, is written in one or more versionsof one or more languages. A web application may be written in one ormore markup languages, presentation definition languages, client-sidescripting languages, server-side coding languages, database querylanguages, or combinations thereof. In some embodiments, a webapplication is written to some extent in a markup language such asHypertext Markup Language (HTML), Extensible Hypertext Markup Language(XHTML), or XML. In some embodiments, a web application is written tosome extent in a presentation definition language such as CascadingStyle Sheets (CSS). In some embodiments, a web application is written tosome extent in a client-side scripting language such as AsynchronousJavaScript and XML (AJAX), Flash® ActionScript, JavaScript, orSilverlight®. In some embodiments, a web application is written to someextent in a server-side coding language such as Active Server Pages(ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), HypertextPreprocessor (PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy.In some embodiments, a web application is written to some extent in adatabase query language such as Structured Query Language (SQL). In someembodiments, a web application integrates enterprise server productssuch as IBMx Lotus Domino. In some embodiments, a web applicationincludes a media player element. In various further embodiments, a mediaplayer element utilizes one or more of many suitable multimediatechnologies including, by way of non-limiting examples, Adobe® Flash®,HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

Mobile Application

In some embodiments, a computer program includes a mobile applicationprovided to a mobile computer. In some embodiments, the mobileapplication is provided to a mobile computer at the time it ismanufactured. In other embodiments, the mobile application is providedto a mobile computer via the computer network described herein.

In view of the disclosure provided herein, a mobile application iscreated by techniques known to those of skill in the art using hardware,languages, and development environments known to the art. Those of skillin the art will recognize that mobile applications are written inseveral languages. Suitable programming languages include, by way ofnon-limiting examples, C, C++, C #, Objective-C, Java™, JavaScript,Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML withor without CSS, or combinations thereof.

Suitable mobile application development environments are available fromseveral sources. Commercially available development environmentsinclude, by way of non-limiting examples, AirplaySDK, alcheMo,Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework,Rhomobile, and WorkLight Mobile Platform. Other development environmentsare available without cost including, by way of non-limiting examples,Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile devicemanufacturers distribute software developer kits including, by way ofnon-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK,BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, andWindows® Mobile SDK.

Those of skill in the art will recognize that several commercial forumsare available for distribution of mobile applications including, by wayof non-limiting examples, Apple® App Store, Google® Play, ChromeWebStore, BlackBerry® App World, App Store for Palm devices, App Catalogfor webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia®devices, Samsung® Apps, and Nintendo® DSi Shop.

Standalone Application

In some embodiments, a computer program includes a standaloneapplication, which is a program that is run as an independent computerprocess, not an add-on to an existing process, e.g., not a plug-in.Those of skill in the art will recognize that standalone applicationsare often compiled. A compiler is a computer program(s) that transformssource code written in a programming language into binary object codesuch as assembly language or machine code. Suitable compiled programminglanguages include, by way of non-limiting examples, C, C++, Objective-C,COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET,or combinations thereof. Compilation is often performed, at least inpart, to create an executable program. In some embodiments, a computerprogram includes one or more executable complied applications.

Software Modules

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include software, server, and/or database modules, oruse of the same. In view of the disclosure provided herein, softwaremodules are created by techniques known to those of skill in the artusing machines, software, and languages known to the art. The softwaremodules disclosed herein are implemented in a multitude of ways. Invarious embodiments, a software module comprises a file, a section ofcode, a programming object, a programming structure, or combinationsthereof. In further various embodiments, a software module comprises aplurality of files, a plurality of sections of code, a plurality ofprogramming objects, a plurality of programming structures, orcombinations thereof. In various embodiments, the one or more softwaremodules comprise, by way of non-limiting examples, a web application, amobile application, and a standalone application. In some embodiments,software modules are in one computer program or application. In otherembodiments, software modules are in more than one computer program orapplication. In some embodiments, software modules are hosted on onemachine. In other embodiments, software modules are hosted on more thanone machine. In further embodiments, software modules are hosted oncloud computing platforms. In some embodiments, software modules arehosted on one or more machines in one location. In other embodiments,software modules are hosted on one or more machines in more than onelocation.

Data Stores

In some embodiments, the platforms, systems, media, and methodsdisclosed herein include one or more data stores. In view of thedisclosure provided herein, those of skill in the art will recognizethat data stores are repositories for persistently storing and managingcollections of data. Types of data stores repositories include, forexample, databases and simpler store types, or use of the same. Simplerstore types include files, emails, and so forth. In some embodiments, adatabase is a series of bytes that is managed by a DBMS. Many databasesare suitable for receiving various types of data, such as weather,maritime, environmental, civil, governmental, or military data. Invarious embodiments, suitable databases include, by way of non-limitingexamples, relational databases, non-relational databases, objectoriented databases, object databases, entity-relationship modeldatabases, associative databases, and XML databases. Furthernon-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, andSybase. In some embodiments, a database is internet-based. In someembodiments, a database is web-based. In some embodiments, a database iscloud computing-based. In some embodiments, a database is based on oneor more local computer storage devices.

While preferred embodiments of the present disclosure have been shownand described herein, it will be obvious to those skilled in the artthat such embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the described system. It should beunderstood that various alternatives to the embodiments described hereinmay be employed in practicing the described system.

Examples Example 1—The Candidate

Steven has been interviewing a candidate named John for a senior vicepresident (SVP) job running a national sales team at his company. Theprocess has been months long and involved many people, personalitytesting, internal discussions and reference calls. The final decisionhas come down to Steven. He is 99 percent ready to move forward, but hasa lingering concern that John may not be the right fit for the job. Hewants to find a person outside of his company that both he and John knowto help him make the final decision. He looks on LinkedIn and sees thatthey have 58 shared connections. The lack of a relationship scorehinders his ability to find the right individual to help him. However,by using the described dyadic ties measurement system Steven is able toquickly locate Sally who the system has determined has a high dyadic tiewith both John and Steven. Steven reaches out to Sally who indeed doeshave firsthand knowledge working with John. Steven is able to have avery candid conversation with her and ask some very pointed questionsabout John's leadership style under pressure. Unfortunately, Sallyconfirms a lingering fear that Steven had about John's personality.Steven thanks Sally for her time, hangs up the phone and call John tolet him know that they will not be making him an offer.

Example 2—The Request for Proposal (RFP)

Mike is the head of sales for company ABC Inc. that is a Workdayimplementer. ABC recently received a request for proposal (RFP) fromnational food service chain, XYZ Inc., to put Workday in every locationin North America. This deal would make Mike's year, but it could alsoburn up half of his yearly business development budget. Currently nobodyat ABC knows anyone at XYZ. What Mike really needs is a connection intoXYZ corporate who can answer some basic question about the RFP. He isnot looking to break any rules or laws; he just wants a “friend” to getsome candid answers before he put 50% of his budget at risk. By usingthe described dyadic ties measurement system, Mike is able to determinethat he has a mutual friend who knows a VP at XYZ.

Example 3—Hiring

A local law firm is looking expand and replace a couple of paralegals.In any business, one of the hardest things to do is to find “good help”.The law firm is looking to maximize their time and search only for toptalent and they want to leverage their current employees to find thesenew paralegals. Unfortunately, there is no tool that will tell them “whoknows who”. However, by using dyadic ties determined by the describeddyadic ties measurement system, the firm can quickly return a shortlistof paralegals that are quantifiably connected to current employees. Thislist can quickly be reviewed and shortened even further throughface-to-face conversations. Based on the conversations, it is determinedthat John and Alice, who are at other firms in town, are far and awaysome of the best at their jobs. The decision is made to go 25 percentover market to hire them away from their current firms.

Example 4—Private Equity

One of the most important things in business is not what you know butwho you know. This is extremely important in the world of privateequity. Eliot runs a private equity firm, Acumen Equity with over abillion dollars invested in various companies. Every company haschallenges and opportunities that arise from time to time. Eliot employsthe described dyadic ties measurement system to create a team wheremembers can share connections among the investors, Acumen Equity, andthe leadership team of the companies that have been purchased by Acumen.For example, company A may be trying to do business with company B.Company A is owned by Acumen. Company B's chief executive officer (CEO)is a 30 year business partner with one of the key investors at Acumen.Using described dyadic ties measurement system, they are easily able tofind this connection that would otherwise be hidden from them.

What is claimed is:
 1. A computer-implemented method for determiningquantifiable measures of dyadic ties, the method being executed by oneor more processors and comprising: receiving contextual data for a userfrom at least one data source; processing the contextual data through afirst machine-learning model to determine quantifiable measures ofdyadic ties between the user and each of a plurality of individuals, thefirst machine-learning model trained with previously received contextualdata of a plurality of other users; determining a grouping for the userbased on the determined quantifiable measures, the grouping comprisingat least one of the individuals; and providing, through auser-interface, access to the determined quantifiable measures tomembers of the grouping.
 2. The method of claim 1, wherein the firstmachine-learning model is retrained with the determined quantifiablemeasures.
 3. The method of claim 1, wherein the quantifiable measures ofdyadic ties are determined based on user contact detail quality, afrequency of communication, information within communications,information capacity and bandwidth, physical distance, social networkties, or timeliness of when contact information was updated.
 4. Themethod of claim 1, wherein the first machine-learning model determinesthe quantifiable measures based on a compounding impact of individualelements from the contextual data.
 5. The method of claim 1, wherein thefirst machine-learning model classifies relationships between the userand each of the individuals according to type, length, and age of therespective parties at a time when the respective relationship began, andwherein the first machine-learning model comprises weighted values forthe classifications.
 6. The method of claim 1, comprising: beforeprocessing the contextual data through the first machine-learning model:receiving validation data for the user from at least one data enricher;and processing the contextual data and the validation data through asecond machine-learning model to determine contact information for theuser and the individuals, the second machine-learning model trained withpreviously received validation data and the previously receivedcontextual data of the other users, wherein the received validation dataand the determined contact information is processed through the firstmachine-learning model to determine the quantifiable measures.
 7. Themethod of claim 6, wherein the second machine-learning model merges theprocessed data to determine and verify current and previous contactinformation for the user and the individuals and to determine achorological order of the contact information.
 8. The method of claim 7,comprising: receiving, from the user-interface, corrections for thedetermined contact information, wherein the first machine-learning modelis retrained with the corrections.
 9. The method of claim 1, comprising:receiving, from the user-interface, instructions to remove the access toat least one of the determined measures.
 10. The method of claim 1,wherein the contextual data is received from at least one source dataprovider via an application programming interface (API).
 11. The methodof claim 10, wherein the at least on source data provider comprises asocial media provides, an email provider, the user's phone contacts, amessaging provider, a provider of at least one forum, a provider of anauction or selling site, or a provider of a recreational site.
 12. Themethod of claim 1, wherein the contextual data is received on a periodicbasis.
 13. The method of claim 1, wherein the grouping includes theuser.
 14. The method of claim 1, comprising: providing, to theuser-interface, an industry strength scoring for the user determinedaccording to the determined quantifiable measures or a regional strengthscoring for the user determined according to the determined quantifiablemeasures.
 15. The method of claim 1, comprising: providing, to theuser-interface, a best path to a decision maker determined according tothe determined quantifiable measures.
 16. The method of claim 1,comprising: providing, to the user-interface, a validation of arecruiting rolodex determined according to the determined quantifiablemeasures.
 17. The method of claim 1, comprising: providing the access tothe determined quantifiable measures to a calendar application or anemail client accessible by at least one of the members of the groupingor the user.
 18. The method of claim 1, wherein the contextual datacomprises digital footprint data for the user and digital path data forthe user.
 19. A dyadic ties measurement system, comprising: auser-interface; one or more processors; and a computer-readable storagedevice coupled to the one or more processors and having instructionsstored thereon which, when executed by the one or more processors, causethe one or more processors to perform operations comprising: receivingcontextual data for a user from at least one data source; processing thecontextual data through a first machine-learning model to determinequantifiable measures of dyadic ties between the user and each of aplurality of individuals, the first machine-learning model trained withpreviously received contextual data of a plurality of other users;determining a grouping for the user based on the determined quantifiablemeasures, the grouping comprising at least one of the individuals; andproviding, through the user-interface, access to the determinedquantifiable measures to members of the grouping.
 20. One or morenon-transitory computer-readable storage media coupled to one or moreprocessors and having instructions stored thereon which, when executedby the one or more processors, cause the one or more processors toperform operations comprising: receiving contextual data for a user fromat least one data source; processing the contextual data through a firstmachine-learning model to determine quantifiable measures of dyadic tiesbetween the user and each of a plurality of individuals, the firstmachine-learning model trained with previously received contextual dataof a plurality of other users; determining a grouping for the user basedon the determined quantifiable measures, the grouping comprising atleast one of the individuals; and providing, through a user-interface,access to the determined quantifiable measures to members of thegrouping.